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speculative decoding

Assess

Techniques

An inference technique that drafts candidate tokens with a smaller model and verifies them with a larger model.

Why it's here

Placed in Assess: 6 article(s) of evidence from 4 source(s), led by research-stage coverage, with 1 in the last 30 days. Confidence 70%.

Evidence (6)

  • 8The New Stack·10/8/2026product_launch
    GitHub Copilot adds local routing, but cloud sending stays unclear

    GitHub Copilot will soon automatically choose between running coding tasks locally or sending them to cloud models, with rollout expected by the end of October. Microsoft also made new sandboxing controls generally available, but it has not disclosed how much repository or conversation context is sent to the cloud or whether developers can force local-only inference.

  • 5Hacker News·9/7/2026research
    Speculative Decoding Performance in vLLM on AMD GPUs

    This article explains how speculative decoding works in vLLM and compares several draft-and-verify methods, including native MTP, Gemma 4 MTP, EAGLE-3, DFlash, and DSpark. The authors report that throughput gains varied significantly depending on the drafting approach, proposal length, model family, draft checkpoint, workload, and token acceptance behavior on AMD Instinct MI300X and MI355X GPUs.

  • 7Hacker News·9/1/2026open_source
    Slotstream runs a 125B Qwen3.8 model on a 48GB Mac

    Slotstream is a new approach for running the 125B-parameter Qwen3.8-Flash-Next model in 4-bit on low-memory Macs, starting from 16GB RAM. It uses expert offloading and SSD streaming to reduce memory requirements while keeping the system mac-native through MLX and Swift, with an auto mode that balances speed and memory use.

  • 7Hugging Face Blog·8/20/2026model_release
    LFM2.5 DSpark Draft Models Deliver Faster Inference

    Hugging Face and Liquid AI released DSpark draft checkpoints for three LFM2.5 models, adding speculative decoding to improve inference speed without changing output quality. The company reports up to 3.18x higher GPU throughput, up to 2.87x on-device speedups, and a 57% average reduction in function-calling latency for LFM2.5-2.6B, with day-one support in llama.cpp and SGLang.

  • 4InfoQ·8/11/2026research
    How to Cut LLM Inference Costs for High-Volume Batch Workloads

    Meryem Arik outlines strategies for building low-cost LLM inference systems for high-volume, non-real-time workloads. The talk emphasizes trade-offs across hardware, inference runtimes, speculative decoding, and queue reordering to achieve major cost reductions.

  • 8Hacker News·6/27/2026research
    DSpark: speculative decoding for faster LLM inference

    DSpark is a research paper describing a speculative decoding approach aimed at reducing latency and improving throughput for large language model inference. It proposes a method that can accelerate generation while preserving output quality, making it relevant for serving and deployment efficiency.